{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# seaborn.distplot() 直方图，质量估计图，核密度估计图\n",
    "\n",
    "解析：\n",
    "\n",
    "该API可以绘制分别直方图和核密度估计图，也可以绘制直方图和核密度估计图的合成图\n",
    "通过设置默认情况下，是绘制合成图，设置情况图下：\n",
    "\n",
    "hist=True:表示要绘制直方图(默认情况为True)，若为False，则不绘制\n",
    "\n",
    "kde=True:表示要绘制核密度估计图(默认情况为True),若为False,则绘制"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "seaborn.distplot(a, bins=None, hist=True, \n",
    "                 kde=True, rug=False, fit=None, \n",
    "                 hist_kws=None, kde_kws=None, rug_kws=None,\n",
    "                 fit_kws=None, color=None, vertical=False,\n",
    "                 norm_hist=False, axlabel=None,\n",
    "                 label=None, ax=None)\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "a: Series, 一维数组或列表\n",
    "\n",
    "要输入的数据，如果设置name属性，则该名称将用于标记数据轴；\n",
    "\n",
    "<font color=red>以下是可选参数:</font>\n",
    "\n",
    "bins: matplotlib hist()的参数 或者 None\n",
    "\n",
    "作用：指定直方图规格，若为None，则使用Freedman-Diaconis规则,该规则对数据中的离群值不太敏感，可能更适用于重尾分布的数据。它使用 bin 大小 $[2*IQR(X(:))*numel(X)^(-1/4), 2*IQR(Y(:))*numel(Y)^(-1/4)]$，其中 IQR 为四分位差。\n",
    "\n",
    "hist:bool\n",
    "\n",
    "是否绘制(标准化)直方图\n",
    "\n",
    "kde:bool\n",
    "\n",
    "是否绘制高斯核密度估计图\n",
    "\n",
    "rug:bool\n",
    "\n",
    "是否在支撑轴上绘制rugplot()图\n",
    "\n",
    "{hist，kde，rug，fit} _kws：字典\n",
    "\n",
    "底层绘图函数的关键字参数\n",
    "\n",
    "color:matplotlib color\n",
    "\n",
    "该颜色可以绘制除了拟合曲线之外的所有内容\n",
    "\n",
    "vertical:bool\n",
    "\n",
    "如果为True,则观察值在y轴上，即水平横向的显示"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\softinstall\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "sns.set()\n",
    "#构建数据\n",
    "np.random.seed(0)\n",
    "x = np.random.randn(100)\n",
    "\"\"\"\n",
    "案例1： 显示默认绘图，其中包含内核密度估计值和直方图\n",
    "\"\"\"\n",
    "sns.distplot(x,kde=True,hist=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\softinstall\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "sns.set()\n",
    "#构建数据\n",
    "np.random.seed(0)\n",
    "x = np.random.randn(100)\n",
    "# 使用pandas来设置x 轴标签 和y 轴标签\n",
    "x = pd.Series(x, name=\"x variable\")\n",
    "\"\"\"\n",
    "案例2：绘制直方图和核函数密度估计图\n",
    "\"\"\"\n",
    "sns.distplot(x)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\softinstall\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "sns.set()\n",
    "#构建数据\n",
    "np.random.seed(0)\n",
    "x = np.random.randn(100)\n",
    "\"\"\"\n",
    "案例3： 绘制核密度估计和地图\n",
    "\"\"\"\n",
    "sns.distplot(x, rug=True, hist=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\softinstall\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.stats import norm\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "sns.set()\n",
    "#构建数据\n",
    "np.random.seed(0)\n",
    "x = np.random.randn(100)\n",
    "\"\"\"\n",
    "案例4：绘制直方图和最大似然高斯分布拟合图\n",
    "\"\"\"\n",
    "sns.distplot(x, fit=norm, kde=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\softinstall\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "sns.set()\n",
    "#构建数据\n",
    "np.random.seed(0)\n",
    "x = np.random.randn(100)\n",
    "\"\"\"\n",
    "案例5：绘制水平直方图 (即在垂直轴上绘制分布)\n",
    "\"\"\"\n",
    "sns.distplot(x, vertical=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\softinstall\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "sns.set()\n",
    "#构建数据\n",
    "np.random.seed(0)\n",
    "x = np.random.randn(100)\n",
    "\"\"\"\n",
    "案例7：改变绘图元素的颜色\n",
    "\"\"\"\n",
    "sns.set_color_codes()\n",
    "sns.distplot(x, color=\"y\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\softinstall\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "sns.set()\n",
    "#构建数据\n",
    "np.random.seed(0)\n",
    "x = np.random.randn(100)\n",
    "# 制定一些绘图参数\n",
    "sns.distplot(x, rug=True, rug_kws={\"color\": \"g\"},\n",
    "             kde_kws={\"color\": \"k\", \"lw\": 3, \"label\": \"KDE\"},\n",
    "             hist_kws={\"histtype\": \"step\", \"linewidth\": 3,\n",
    "                       \"alpha\": 1, \"color\": \"g\"})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "整理制作：数据挖掘分析可视化学研社：\n",
    "\n",
    "<div>\n",
    "<table align=\"left\" border=\"1\" bordercolor=\"#000000\">\n",
    "    <div>\n",
    "    <tr>\n",
    "        <td>\n",
    "            微信公众号：数据挖掘分析可视化学研社<br>\n",
    "            <img src=\"../image/visual.jpg\" width=\"150\" height=\"150\" align=\"left\"/>    \n",
    "        </td>\n",
    "    </tr>\n",
    "    </div>\n",
    "    <div>\n",
    "     <tr>\n",
    "        <td>\n",
    "        配置环境：python 3.4+  \n",
    "        </td>\n",
    "    </tr>\n",
    "        </div>\n",
    "</table>\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
